Being a data scientist is a hugely rewarding career. Data science is becoming more necessary in a wide range of companies. It is becoming a popular career choice for many. So, what exactly does the job of a data scientist look like? In this article, you will know, what kind of skills we need to become a data scientist?
Data scientists are well educated. The most common fields of study are Mathematics and Statistics followed by Computer Science Engineering. After completion of a degree programme, you are not done yet. The fact is, most data scientists have a Master’s degree or Ph.D. and they also undertake online training to learn a special skill like how to use Hadoop or Big Data querying. So, you can enroll for a master’s degree program in the field of Data Science, Mathematics, Astrophysics or any other related field. Skills you have learned during your degree programme will enable you to easily direct to data science. Apart from classroom training, you can practice what you learned in the classroom by building an app, starting a blog or exploring data analysis to enable you to learn more.
Nowadays R is the most popular language. As R is an open source programming language, available as Free Software under the terms of the GNU. R is specifically designed for data science needs. We can use R to solve any problem we encounter in data science. Majority of data scientists are using R to solve statistical problems. However, R has a steep learning curve.
Python is the most common coding language mostly required in data science roles, along with Java, Perl, or C/C++. Because of its versatility, we can use Python for almost all the steps involved in data science processes. Python has the capability to take various formats of data and we can easily import SQL tables into our code. It allows us to create datasets and we can literally find any type of dataset we need on Google.
Data Scientists may face a situation where the volume of data exceeds the memory of the system or we need to send data to different servers, this is where Hadoop comes in. We can use Hadoop to quickly convey data to various points on a system. You can also use Hadoop for data exploration, data filtration, data sampling, and summarization. Of course, this isn’t always a requirement, it is heavily preferred in many cases.
Though Hadoop became a large component in data science, it is still expected that a candidate will be able to write and execute complex queries in SQL. SQL (Structured Query Language) is a programming language that can help us to carry out operations like add, delete and extract data from a database. SQL also help us to carry out analytical functions and transform database structures. SQL is particularly designed to help us access, communicate and work on data and it also gives us insights when we use it to query a database. It has concise commands that can help us to save time and lessen the amount of programming we need to perform difficult queries. Learning SQL will help us to better understand relational databases and boost your profile as a data scientist.
Apache Spark is one of the most popular big data technologies worldwide. It is a big data computation framework like Hadoop. A major difference is that Spark is faster than Hadoop. Apache Spark is designed for data science to help run its complicated algorithm faster. Spark helps in disseminating data processing when you are dealing with a big sea of data thereby, saving time. We can use it on one machine or cluster of machines. Spark also makes it possible for data scientists to prevent loss of data in data science. A strength of Apache Spark is in its speed and platform which makes it easy to carry out data science projects. With Apache spark, we can carry out analytics from data intake to distributing computing.
A major portion of data scientists is not proficient in machine learning areas and techniques. ML skills include neural networks, reinforcement learning, adversarial learning, etc. If you wish to stand out from other data scientists, you need to know Machine learning techniques such as supervised machine learning, decision trees, logistic regression etc. Imparting these skills will help us to solve different data science problems that are based on predictions of major organizational outcomes.
The business world produces a vast amount of data often. This data needs to be translated into a format that will be easy to understand. People are familiar with pictures in forms of charts and graphs more than raw data. As data scientists, we must be able to visualize data with the aid of data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau. These tools will help us to convert complex results from our projects to a format that will be easy to comprehend. A lot of people does not understand serial correlation or p values. We need to show them visually what those terms represent in your results. They can quickly grasp insights that will help them to act on new business opportunities and stay ahead of the competition.
It is critical for a data scientist to work with unstructured data. Unstructured data is an undefined content that does not fit into database tables. It includes videos, blog posts, customer reviews, social media posts, video feeds, audio etc. These are heavy texts lumped together. Sorting this data is difficult because they are not streamlined. Dealing with unstructured data helps you to unravel insights that can be useful for decision making. As data scientists, we must have the ability to understand and manipulate unstructured data from different platforms.
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